static void Main(string[] args)
{
string fileName = "C:\\Users\\Vince\\Documents\\Dropbox\\Dev\\VStat\\Test Data\\letter-recognition-normalized.csv";//"C:\\Users\\Vince\\Documents\\Dropbox\\Dev\\Neural Network\\iris_converted_normalized.csv";
int inputs = 16;
int outputs = 3;
//string fileName = "C:\\Users\\Vince\\Documents\\Dropbox\\Dev\\VStat\\Test Data\\iris_converted_normalized.csv";//"C:\\Users\\Vince\\Documents\\Dropbox\\Dev\\Neural Network\\iris_converted_normalized.csv";
//int inputs = 4;
//int outputs = 3;
//string fileName = "C:\\Users\\Vince\\Documents\\Dropbox\\Dev\\VStat\\Test Data\\debug.csv";//"C:\\Users\\Vince\\Documents\\Dropbox\\Dev\\Neural Network\\iris_converted_normalized.csv";
//int inputs = 3;
//int outputs = 1;
//create the data source off of an existing file
TextDataSource dataSource = new TextDataSource(fileName, inputs, outputs, true);
dataSource.setValidationMode(ValidationMode.Holdout, .2);//(ValidationMode.KFold, 3);
// dataSource.setValidationMode(ValidationMode.KFold, 4);
dataSource.loadData();
//create the neural network
//BasicNetwork nn = new BasicNetwork( 4, 3, 1, new int[] {10});
BasicNetwork nn = new BasicNetwork(inputs, outputs, 1, new int[1] {2});
nn.printConnections();
//train the NN
Console.ReadLine();
VStats.Trainer.BackPropogation trainer = new BackPropogation(nn);
trainer.train(dataSource);
//serialize and save the model
IFormatter formatter = new BinaryFormatter();
Stream stream = new FileStream("MyFile.bin", FileMode.Create, FileAccess.Write, FileShare.None);
formatter.Serialize(stream, nn);
stream.Close();
Console.ReadLine();
}
private void trainButton_Click(object sender, RoutedEventArgs e)
{
string fileName = fileTextBox.Text;
bool header = (bool)headerCheckbox.IsChecked;
trainButton.IsEnabled = false;
System.Threading.Thread newThread;
newThread = new System.Threading.Thread(
delegate()
{
logTextBlock.Dispatcher.Invoke(
DispatcherPriority.Normal,
new Action(
delegate()
{
logTextBlock.AppendText("Initializing file: " + fileTextBox.Text + "\n");
}
)
);
int inputs = 4;
int outputs = 3;
TextDataSource dataSource = new TextDataSource(
fileName,
inputs,
outputs,
header
);
dataSource.setValidationMode(ValidationMode.KFold, 4);
dataSource.loadData();
BasicNetwork nn = new BasicNetwork(inputs, outputs, 10);
BackPropogation trainer = new BackPropogation(nn);
trainer.epochIterationComplete += new Trainer.EpochIterationComplete(trainer_epochIterationComplete);
trainer.train(dataSource);
trainButton.Dispatcher.Invoke(
DispatcherPriority.Normal,
new Action(
delegate()
{
trainButton.IsEnabled = true;
}
)
);
}
);
newThread.Start();
}